Table 1. Comparison of Information and Decision Support Systems

"... In PAGE 4: ... Within these information systems lies the data that is needed for decision-making. The following table ( Table1 ) is a summary of the types of information systems, taking note that it is not possible to list all the types of information systems that are in use. Name Characteristics Specific Area of Use Source Transaction Processing System (TPS) - Collects and stores data about transactions.... ..."

"... In PAGE 2: ... In the development of our own RoboCup team [10], we are investigating the use of template decision situation mod- els, since the agents repeatedly find themselves in the same kind of problematic situ- ation. Table1 shows to what extent the discussed functionality is supported by inter- faces of current tools suitable for agent interaction, and also what kind of decision analysis techniques, out of belief networks (BN), influence diagrams (ID), decision trees (DT), AHP [12], and SMART [7], they implement. Table 1.... In PAGE 2: ...All tools in Table1 are based on the principle of maximizing expected utility (PMEU), except the last two, which are based on AHP and SMART. Consequently, for a given decision problem, the advice supplied by each of the PMEU-based tools will be the same, thus making response time the discriminating factor.... ..."

Table 5. Issues in portability of Decision Support software tools

"... In PAGE 28: ... Another important reason that DST are not always transferable between countries is that unless the tool has received extensive documentation, application, verification testing and peer review in the country its use is proposed in, the quality of the tool for use there may be difficult to judge. Table5 presents the key transferability issues, providing examples in terms of analysis of soil or groundwater contamination. However, the major issues still apply to other types of analysis (e.... ..."

"... In PAGE 40: ...ee) their correlated, external outcomes. For this we use the exitdec action. Outcomes which nish but are not nal do not trigger anything. To incorporate this type of outcome partiality, we use a transient action finish, which as axiomatised in Table9 , raises an exitdec if no other execution is left. Since the external actions correlated with nishing outcomes can be any type of processing element, the parameter of a finish action has x 2 X .... ..."

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Table 2. Three types of AI technologies for decision-making process

"... In PAGE 5: ... Comparison of Information and Decision Support Systems Using Artificial Intelligence to Support Decision Making The use of artificial intelligence (AI) to support decision-making is a relatively new field, although expert and knowledge based systems have been designed based on AI principles. Table2 summarises three types of AI technologies that can be applied to the decision making process. Name Characteristic Source ... ..."

Table 6: Complexity of operations on decision diagrams.

"... In PAGE 37: ... It is well known that using a different branching factor, or representing a function by a vector of diagrams, has no effect on the complexity of the operations used during model-checking [41]. The middle column of Table6 summarizes these complexities of the operations from the left column with respect to the size of the graph representing the diagram. Note that even though we can think of representing an mv-set using a vector of diagrams, the underlying implementation constructs a single directed acyclic graph.... In PAGE 37: ... Moreover, since the underlying graph is connected, we can express the complexity of operations relative to the number of nodes in this graph. These complexities are given in the right column of Table6 , where a9 is the number of nodes and a33 is the branching factor of the decision diagram. Using this representation of complexity, we infer the expected running time based on the empirical evidence on the sizes of different decision diagrams.... ..."

Table 1 summarises the major types of these systems and their role in decision support.

"... In PAGE 22: ... 5. Considering Potential Applications In order to consider the different types of diagnosis and instructional support presented in the previous sections, it is useful to list some potential uses to be considered (see Table3 ). These are just preliminary ideas; researchers should determine what their goals are in developing a diagnostic system.... In PAGE 22: ... These are just preliminary ideas; researchers should determine what their goals are in developing a diagnostic system. Is it to provide homework help, thereby creating a completely different product than it has done before? To create a diagnostic tool that teachers can use to place and group students, to provide remediation for students who need help and to provide challenging material for those who are ready? Or is it software or a web-based subscription that parents can buy for their children to help them with math, reading, or writing? Table3 lists some of the features to be considered for each of these types of systems, including whether it needs to do diagnosis, provide instructional support, be connected to a curriculum, or measure achievement of KSAs and if so, to what extent. These features may be able to help determine which approaches to take.... ..."

Table 4. Uses, targeted end-users and current status of major decision support tools used in Agroforestry.

"... In PAGE 10: ... Table4 . Continued Decision support tool Intended use Targeted end- users Current status and availability BEAM (Bio-economic Agroforestry Model) Bio-economic assessment of agroforestry systems used for research and develop- ment projects Researchers Available through University of Wales, Bangor, UK http://www.... In PAGE 12: ... (2002), using a modified version of BEAM to assess the impact of Indonesian rubber production under uncertainties of prices and climate, concluded that as a risk aversion strategy, it was bet- ter to use lower planting densities, undertake longer rotations and start tapping later in the life of the trees. Many of the models presented above, and as noted in Table4 , are complex, predominantly used by re- searchers, and not very friendly to the layperson. Al- though some have been applied outside research (for example, BEAM and SCUAF), there is little evidence of use by decision makers, planners, extension agents and landowners.... ..."

"... In PAGE 12: ....8.4. Mathematical modeling Another measure of the support provided by the MKIS is how well it supports mathematical modeling. Table7 indicates that new product evaluation and sales/demand forecasting are the two most frequently used models, and that analyzing sales profit and sales/ demand forecasting receive most of the MKIS support. Fig.... ..."